Accurate process variation coverage within SPICE models has become an essential demand from the design community. These variations can be simulated most efficiently with statistics based models which avoid unrealistic pessimistic corners. Sufficient measurement data for reliable statistics is not available in the early process/device development phase. During this period technology computer added design (TCAD) simulations can provide such data. We will present a method to support high quality correlated statistical SPICE models based on TCAD generated statistics. We describe a method to identify a minimum number of required process variables for TCAD simulations to accurately represent the process variations. Afterwards we will show how to create extended statistical data which conserves the underlying multivariate statistics. Taking into account the covariance matrix enables physically realistic statistical models which reflect the most important process correlations. This will be shown on the example of a high-voltage n- and p-channel LDMOS transistor implemented in a HV-CMOS technology. Finally the SPICE Monte Carlo results will be compared with the underlying process and device simulations. Additionally the usability of the method will be proven with comparisons between TCAD, SPICE simulations, and existing measured data.